Abstract

The work described in this Thesis is concerned with developing a technique to
visualise the electrical activities of the heart in 3D from ECG signals taken from
sensors on the surface of the body. Generating such visualisations can help
cardiologists to identify abnormal electrical propagation flows non-invasively.
The approach taken has been to develop models for the body and the heart,
followed by the implementation of the 'forward' solution, which calculates the
body surface potential for excitations within the heart. The results obtained match
published results. The 'inverse' solution, which determines the heart
electrophysiology from the body surface measurements, was then implemented.
Values derived from the 'forward' solution are then used to confirm the accuracy
of the 'inverse' solution.
The study has led to three improvements to existing approaches: a more realistic
model of the heart's conduction system; and more effective solutions to both the
'forward' and 'inverse' calculations used to determine heart electro physiology.
The methods that were developed were based on the biological and physiological
properties of the heart tissues as well as the working methodology of the Diffusion
Tensor Magnetic Resonance Imaging (DT-MRI) scanner. Evaluation of the
proposed techniques has been verified using seven methods, which include the
location of Purkinje cells, the anatomy of the ventricle conduction system of the
human heart, the Myocardium tissues to the Purkinje tissues ratio, the excitation
propagation of the conduction system of the ventricles, the excitation isochrones
of ventricles of the human heart, the generated body surface potential map and the
generated 12 Lead ECG electrograms.
A fundamentally new aspect of the work is to extract the conduction system of the
ventricles from the DT-MRI providing a more realistic model for this structure,
and this process has been accomplished by a semi-automatic manner, where
extraction of the conduction system is accomplished with minimum manual processing and some simple image processing techniques. The Monodomain
reaction diffusion equation, which is used to model the ventricle excitation
propagation, has been updated to include the diffusion of the electrical stimulation
in a non-uniform material, which is the more realistic case.
The DT-MRI modality was employed for the first time to model the conduction
system of the ventricles and to determine the relative non-uniform conductivity
distribution inside the heart Myocardium. Unlike previous methods which
consider an estimated conduction network for early activation points and assume
the Myocardium material to be a uniform material, the new approach provide a
more realistic solution for both the modelling of conduction system and the
'forward' solution.
The 'inverse' solution is calculated for a localised multiple dipoles (sources)
distribution inside the heart Myocardium based on transmembrance potential
instead of current density, as currently used. This type of problem is highly illposed
as the number of body surface readings is much fewer than the number of
the heart dipoles (highly underdetermined). Employing the transmembrance
potential reduces the size of the problem to a third of the current density solution
and as a consequence it improves the localisation (due to the reduction of the
underdeterminity) and reduces the memory usage and computational power to be
1/9 of the current density solution. Three equations have been derived to calculate
the transfer matrix of the problem: the first one is for an isotropic source in a
homogeneous-isotropic conductor; the second one is for an anisotropic source in a
homogeneous-isotropic conductor; and the third one is for any type of source in an
inhomogeneous-isotropic conductor. A low resolution version of the 'forward'
model has been employed to simulate the 'forward' solution of the heart, and the
body surface readings (200, 100, 64 and 32 electrodes) of that model were then
used in the 'inverse' solution. Three regularisation techniques have been used to
solve the Inverse Problem namely: the Minimum Norm (MN), the Weighted
Minimum Norm (WMN) and the Exact Low Resolution Brain Electromagnetic
Tomography technique (eLORETA). Results of these methods are compared to the
original pacing points (1, 2, 3, 4, and 5 pacing points) which are used in the
forward solution. It is concluded that the best results are obtained from the eLORETA method, and a
large number of electrodes on the body surface and fewer number of sources leads
to better results.